{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e9a177b-e86d-4820-a921-eaca5daaf45e",
   "metadata": {},
   "source": [
    "# 读取NC文件并绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a92abab5-7c88-4440-9d74-d40a1834a301",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['sig', 'lon', 'lat'])\n",
      "(42, 720, 360)\n"
     ]
    }
   ],
   "source": [
    "# 读取字典信息\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 打开下载的.nc文件\n",
    "nc_file = nc.Dataset(r'C:\\Users\\calibre66\\Desktop\\WaterYield1981-2022\\sig_05deg_yearly_1981_2022.nc', 'r')  # 'r'表示只读模式\n",
    "\n",
    "print(nc_file.variables.keys()) #dict_keys(['wy', 'lon', 'lat', 'time'])\n",
    "e = nc_file.variables['sig'] # E是三维数组\n",
    "\n",
    "print(e.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bcd4220",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720,) (360,)\n",
      "(360, 720) (360, 720) (42, 720, 360)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 打开下载的.nc文件\n",
    "nc_file = nc.Dataset(r'C:\\Users\\calibre66\\Desktop\\WaterYield1981-2022\\wy_0p5deg_yearly_1981_2022.nc', 'r')  # 'r'表示只读模式\n",
    "\n",
    "# print(nc_file.variables.keys()) #dict_keys(['wy', 'lon', 'lat', 'time'])\n",
    "latitude = nc_file.variables['lat'][:] # latitude与longtitude都是一维数组\n",
    "longitude = nc_file.variables['lon'][:]\n",
    "X, Y = np.meshgrid(longitude, latitude) # 合并成二维矩阵\n",
    "time = nc_file.variables['time'][:]\n",
    "wy = nc_file.variables['wy'] # E是三维数组\n",
    "\n",
    "print(longitude.shape,latitude.shape)\n",
    "print(X.shape, Y.shape, wy.shape) #查看数组格式\n",
    "\n",
    "plt.contourf(X, Y,  wy[41, :, :].T) # 1981-2022, 42年，# 该文件 经纬度是反的\n",
    "plt.colorbar(label=\"wy\", orientation=\"horizontal\")  # horizontal, vertical\n",
    "plt.show()\n",
    "nc_file.close() # 关闭nc文件\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b9757b2-c66c-476a-8647-7b6636783554",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720,) (360,)\n",
      "(360, 720) (360, 720) (42, 720, 360)\n"
     ]
    },
    {
     "data": {
      "image/png": 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3w++3LwOKZTblWE4O5dOrlKlrjRRr1eixWjPrhhtc0YqbghnjJ5kl0nF34+jC8SKWUpFwDbwjXa/Kc9iMRvHxyC3VKqSflflMqOvFK3mbpSU8v//977Fv3z588MEH8nvjxo3D2rVrsWbNGggh0KlTJ/zxj3/EhAkTAJyM+LKzs7Fo0SJdjZbtjhCV9PT2seo3jTR6DkdPCY1ZwU0smasV3b+t4JWMwQ7qUkz1OWyklEf93XDnmvp8rlxZLpfmSIGP1IaHAQG5iZOl3lb8tqfa8IwbNw4ff/wxHnnkEezYsQMvvfQSnnnmGZSVlQEAkpKScM899+Chhx7Cv//9b2zZsgU33HADOnXqhCuvvNLKpMVEq+2GXTe69c+N13yijbYHj1SPHKq9jtnBjnLdRkj73KwRdK3CNhz2q54yrkWbAemcjvZYK9vHMdghN3BTFX+8sjTg+cUvfoHXXnsNL7/8Ms4991zMmDEDjz32GEaMGCEv86c//Ql33nknbr75ZvziF7/AkSNHsHz5ctvH4NEr3JgsyhPSyptq9ZRxaOjcKL8koRo466UMfoxm8sogRhksRWroHE0jPTOCHwY9zlM3Oraq273ecyxRq7XI3axqAJyI+VRCzKVlFrc1VO2y+FEAgH+vL+h9u3vzxNqzwKwnaDNmSDeL0+dGvCgcMyfklBnR7EP1cdVqbK+n7U4i3gzIXZyaIy8UL1RpMeDxgEhtIaxmVldKO8byceJGxuAnmJUN/7UCHrVIJTkMdqLn5YHznOCGkuhQwy+YkTZP9dKKV27qHaSH1FOleso4FMyYKz8x2xX4hJt41Mg6rObUjYw9uE7Ss//VvQ6N7jt1zyxlg2Tpb71pIf20pqIxMmo7gyRt8XYvcjuW8PxXPJ9Yysxbqx1ErKPQ2jHdhFUZnl3TQugRj+eWmSJ1eZWqt7TOV737zuljnMicmlA4kSgDerOn0FH/jh1YwkNRUZbqqIOeWMbqMdI1V2uMHqsnQI3ETYGsm9LiNuHmEIv0HXIvPQ9MLOHRz8xzPhGnQbG0l1Y8cetQ2Hqoe4fpGXLfSA8po8Ofa2VeoTI0OzO6eD2+XhBrxqrulRfL+vT2IiT9Yt2HXjoG8XxOmXkftHu6Jj1YwuMR6iBH+ltrUDYg9kBD71OZcrlQvWW83H6HQjOasUrnS8mgCsDAOaM+V1maYB4zbuxePR6JXnLlxgdMlvB4nFkNl43Mo2W05Ii8TzlWVbRPkXXd/SgcM8fwecPzzBp6BiW1at+7tQRF+RBnda9TswdhdWOAYjaW8JBuWr2x1E8xWhlRoj7pGJlxOBHonayQs5DHBz29M6269q1YZywlzvE8BUkiBDoSBjwet/658UHj9MRa4qO34TLQsqtqPGYGoWg1+NN7o2Y39WDqfSHNY0X6OXXDlYIep45XNFPfRNpX0bRR1PvbsWCeETtWaSUAZfseq4bv1+LleYj0lDiEW8aNDfqcJu0Tr54zVjGreifa9YRrHxXr3HuhGgCHez+SUHMH6l3eye73zDNiwxKeBKFsxGz3wIRWckPXSj55kVO0hoAw8l2tG3+k9np6SnljCcKUwY4d3NoeKBzmOdFhwEOmi9SY2YynIXVjO6eCHjMG/2LmpZ9bqka1zmUnqpXM6m0Z7qYfTUBgpGopmvWES7PR4xBprDA3BETKRv9OP+DFM460nICkNj3RlPAYyUyirQuPR+oGynob6IZbNlFFasNjRdBj5ojidg20Z+Z6jDJr/0ea3DXUb1nVEzSWIMwqoXpmeSHf4EjLZLloq7LMfoJ1y9O6GfSW1Hghk7KaNP+VnY1DjY4obiRISP+6QZ7DSzmvl9ZykQI9QHvyU70NvbW2MZoqKLPyglBd2Y10eHB6NHcrMb8wFwMe0i3WUh0vZ0wAGxSaqXJlue37U31+Gr2ZKwMRKbDRaiAbSyCnDnbCBVBKVlS3Wdl9W28PqlgDN63fjYf8iYFQdBjwkGn0zqFlR6YiPVXbedNkJmQd9Y0ymhm59Z4PoapQwp3T6kBE+VtmnOtaXfe10ghoj2iuZ5b4UD2R1O/H2vMqlvVolf6oP1e+H83vROqRZYdIeQnb/kWHAQ+ZSutpNlymY1VDTztHOWXGY71QQbKegS8lJYMqsD6KIDhSryGtKiZA+7yINggvGVTR4ne0ArBwox2XDKpAusb7Wt+zojpRb48w5edG0mYFJ0p89OYnesYBo2AMeMhyeqvCosnIwvVaMHtmYSPvk7ZYj4mesVekNkBapKDHyOCGZlfFRjMCtzqtynZOem7Kequ+rBKu9ClSwKpVuhdLQ+ZYODm4IKvMY2dbL62ZM2eivLwcd999Nx577DEAwLFjxzBhwgQsWbIEDQ0NKC0txfz585GTk6N7veylZYzbLppoRkpVsrqbJgMac5kV8IQqVQFa3ty1AhY9VTyxpkMPI7+vrJoCQm9nqO+GY1b3dr3LRvp9M9rimCWWsY6MMNKzU+933c6TvbQ++eQTPP300+jTp0/Q++PGjcObb76JV199Fenp6bjjjjtw1VVX4aOPPrIjWQnJa+M4eGlbKDwzn+ClYGF9jIFPqHWHa0ejZKS0p667P6hqK1xJlvJ3Q5UCmVldY1WA4WQ6AOtKjaINUJT5d7wGOU6yvITnyJEj6N+/P+bPn4+HHnoI/fr1w2OPPYa6ujpkZWXhpZdewm9+8xsAwPbt23HOOeegqqoKv/zlL3WtnyU8xpnxlG3V+B9u6qbODMV8es+9cDcavaU74UpDtNapTptyUL5wbdCs7K0U6rvqbYvUld+M341VpOqrUPvaSNBhdeAV6/qjzVO82mbQcyU8ZWVlGDZsGAYPHoyHHnpIfn/Dhg04fvw4Bg8eLL/Xo0cPdO7cOWzA09DQgIaGn0/EQCBgXeJJk1mZipGxT+zmpUwlHmndGGOpPoo0Dk7JoAogTLsQs0tHYj3f1Y2Y9bbRses6MxIsRNOLSs+oyLEEf256GGMptnksDXiWLFmCTz/9FJ988kmLz2pqauDz+dC+ffug93NyclBTUxNynRUVFZg+fbrZSaX/8tJggEYxyHGvWNvKKNcTTQPeUGPsuImT6YnmwSXaUjOzR0OOtn1TIueV8cqy2dL37duHu+++Gy+++CJat25t2nrLy8tRV1cnv/bt22fauhNFuBu73RcwMwwKJ1JD3WhojZlTubI87HUh/a5dvZ0iVekpt8GsYDBa6nFxoikdUX9P+tvKaS+ktk3S8sp/1Z+p07r+ufFh22Rpvc8HKudZVsKzYcMGHDp0CP3795ffa2pqwvvvv48nn3wSb7/9NhobG3H48OGgUp7a2lrk5uaGXK/f74ffz5ukl7gh6GFm5E5aDXXDMdJ7S8v658aHrfaKtoTIqGhGeS4cM8e2HkVKTlZNm1m9rmd96gBcb3VrtAMFsjrLXJaV8JSUlGDLli3YtGmT/CosLMSIESPk/7dq1QqVlZXyd6qrq7F3714UFxdblSz6L97gye2kJ3wzSjBCBSla65be0xr7xk6hSje8chM0Worj5ACE6t8sHDMHhWPmoK67X/d5YfS4caJh81lWwtOuXTuce+65Qe+deuqpOO200+T3R48ejfHjx6NDhw5IS0vDnXfeieLiYt09tCg2rIMO3V2YrGHknIv2uEjBTSylQeGW1dsjykrqEoNwoygrWTmyuVbwEikdRmil3Yk8TB2IqI9FNANLkj0cHWl57ty5SE5OxtVXXx008CDZQyvjZhBEVlL2llJ3BQ91E9QaYC/UsnXd/cB/P9eqCjMSDOkZ40b+zQjLGb2m9MwFpZW+SL9j5VQJWus2sw1OpLZB8Zh3qYMlBknWsm2kZatwHJ7YRbrpuLn7uBlYTOwcI+Pm6LkRRurOrjfg0Uqj0RIn5feiuZEZCaj0LBft8ka5aZRkK+mZ4DPcd6I5J7yWV3luHB5yP+XonaGCG2WRdbxkSHp4LQOJN8pzKdzs4tGWkKhFW01W192ve4RbqdQ0PexS+n5TzzUXzTg2bhVpoEe3iDXfYLDjDAY8BEBfcbBWPb3bMyZyr1CD/cVyTmlNJBordTVcNN9NJE4PyhgtI4GW1CNO7/GNpWSHgY55WKVFLYQbWE3vcPBuZtbEjxQdswfuU04BoVyvW46z0w2cnRCPAU80Is1urgyK9AQ69flJqJ4yzrT0uR2rtMhx6gae6huIUjwFO+yR5bzCMXM0S3ViEWoMFWUXYomec0DZfkgKpGI5b+y4Ptx2HVrZODqeaAU79flJAIB2e06WNbAExz6WjcND3qHVjiDcJIBuxGDHeXaNUhyO1BYn3OCC4UbZNcqKbQ41do1Z12E8XM9uEa7URt1VXSuwYbBjL5bwkG7qod9DfR7NPDZWPp0y2CE1IxNZKhssG60mM+O8DlV6o3zfzGvOzNGLzRpvx0tYsuMctuGhsJTtD8INu6+u9tKTWekdpCxWzFjcwc1tWaSg2Iw0WlG9pF5ntEGB2RNvRvN7egMht54rWozkMXp7+yUCtuEh1wpXPG/kSS7Ssm5rj0DeJ83Z5cbzTjkIoRnrUQvXRi/UepTfidRlXqtXp9Hf9BIGOs5hwENhGZ0sMZbGilLmqeemo5VphKpPj3biPjKPW4MJJbMnorSCskOB0RGe9fS61EN9jZs9ThCRVRjwUETKoCfisP6ILYNTrttouxsO0e4Ol3csw7KD8+S/4yHYUYu2PZrekg8jol2Pnu9FCogkTnRaiJeSXj5MxQ/20qKILu9YFnEZMzOmypXlpjcydkMPIS8rGVTRYh9L7+mdGsHoZ1ZRl15o9YqKNMu31iCdZlBOV2HXvrGyBEfZI07rpSZ16SaKBgMeiprZQYmZgY5dDaLpJOVxW3Zwnu7BBePlKR4IDn6kG3Kk9jVmByZSWyOj6473gQDr85PkYMdNQQ9Ld+ILAx6KaNnBefINTZlxKofaD/UkrCVU118zsRu6/UINWKmkPE+0ggWnpy4xev4qRxoPNfChFUGPpHJlOdY/N77Ffg1VKuVmUlCjDG60SN26iYxiGx7STX1DU75vpJ2GXeNsxFPpgVeEOg8iVf+E+1trPVYc10ht00JRf64879QPBWalWz3LvFaAXzKoImTQ4/broqFzIxo6A/69PrTbI1wZ5LB0J/6whIcMq1xZLj/RRtM2xqxRbCMJVcrD9jzupSe4MGswP63SEOX6o/0tdQlQNJOOxqJwzBwUjpkT9jeNlMjaRSuwaejc6FBqwpOCHXaOiC8s4aGoKJ9WL+9Yhsr/ttsI93Sv58ndbHwKs188lCIYLbWJlfoaiXUfheotqZyBPppAyw096k4GPT40dG6Ef68PgPtKdwAGO/GIAQ9FRVmNlY4zWrwHtAxi4uFGSNFTl5yppz4INequF7XY9v9eI9JnWssZJX0v6MaruvaUDZz1MGu06VidLO1pBbcFOyzZiW+s0qKoKRtjqnuOaI3bEaqKwKrSHalbtFabIzKXnmpCM6qL3CpUI2y1ypXl8G3/Rv7brn1gpBpXaghNwbhP4p+lAU9FRQV+8YtfoF27dsjOzsaVV16J6urqoGWOHTuGsrIynHbaaWjbti2uvvpq1NbWWpkssoAyiFD+X8+NzcxMv2RQhdyGQfn7yvfIXMpeWXoDGTe1HYmFuh2MnobPykEZ7dwPRtuuSb2/jIjUwypeMdjxBksnDx06dCiuvfZa/OIXv8CJEydw7733YuvWrdi2bRtOPfVUAMBtt92GN998E4sWLUJ6ejruuOMOJCcn46OPPtL1G5w81L2MBhhGMxWt4nqtTD1UqRJLesyhVQWSqNWXUslmqO1XnuN6xyoy8ttalFXMRs/5WB4S6vOTXNm7Si8GOdbz1OShy5cvD/p70aJFyM7OxoYNGzBw4EDU1dXhr3/9K1566SVcdtllAICFCxfinHPOwccff4xf/vKXViaP4pBWewXlXFnhMnTljVk9gBtRrPTMXWV3jy0gtlIkrTZGUgmOmcGMkbnxzPxN5W8wwPE+W9vw1NXVAQA6dOgAANiwYQOOHz+OwYMHy8v06NEDnTt3RlVVleY6GhoaEAgEgl7kDdFmcHozdGWbI+VgcVrtfMiYUCNbe6Xqyig3DpgYTaClHjRR7/E0Uq0VKtCwMgBRr5vBTmKwrZdWc3Mz7rnnHlx44YU499xzAQA1NTXw+Xxo37590LI5OTmoqanRXE9FRQWmT59udXIN44zc5gj3BBwqIDKSkSsHT1RPVKoMepQ3LB7X8NT7TpJo1VlGt1fab1YMkKkOOGMpVVJ/t8viRwHgv72otLXbI0xpyyOVwphZqsSeVonLthKesrIybN26FUuWLIlpPeXl5airq5Nf+/btMymFZLZonu5DZfyhSmCiDUak0WmVo9RK/6rTwAbPZBWtKVtipT5/naqyjRScrH9uvK7rV7lMrEEUH14Smy0lPHfccQeWLl2K999/H2ec8fN4FLm5uWhsbMThw4eDSnlqa2uRm5uruS6/3w+/331PjryQWlKWphh5gtUMLkx8Ataqt5fTGOF3WOdPsTBr5GU9vcHc3DDf6LVTPWUcCmbMBWBNG6JoMC+IP5YGPEII3HnnnXjttdewatUqdO3aNejzAQMGoFWrVqisrMTVV18NAKiursbevXtRXFxsZdLIRnomldSifJqzInNTV2lJpIzMzTeMROHF3l7qoCeaqi09o5Yrx/sx0+6Rk9Bl8aNR98IyKziINfCJJR0s8Y1PlgY8ZWVleOmll/DGG2+gXbt2cruc9PR0tGnTBunp6Rg9ejTGjx+PDh06IC0tDXfeeSeKi4vZQ8uDjAY+yoxMnbnFUrQdKaML9zmf5Ozh9cbOoYIes1gdqJ+c8sEYsx8gfp56Qp/6/CS5lKh6yriYf9+L4w15naUBz4IFCwAAl156adD7CxcuxKhRowAAc+fORXJyMq6++mo0NDSgtLQU8+fPtzJZ5DCt0hS9QmUybDTuLCsa3XqxdMcK8VASadW12dC5MWzj6VCYXyQmy6u0ImndujXmzZuHefPmRVyWvEfZhkapcmW5HAyFe5IqmDFXLvVhnbozlBNWmsnrgY5We55oemw53Sg5UpqtKK2rnjJO7i2mt2rN6TY/5DxOHkquoJVpS11Stbq4hsq8pOUKZsw1pdjaqEQczNBrgYmdJUuxNmJ2quovuGo59H4y88Ej1PXsVCmPlC85kc9QdDh5KLmalCGdnD355xeRVxgNeqTu3OufG+9YYL3+ufGOt2HZPXISupzxHwDOtqcpmDFXbhtE7sYSHnI95VOY3jY/TgVFiVSy40VOlZgopzrRYsYAglZo6NwIwOeqhxA7u60rS5/NbBBN1mDAQ3FF3eYnVLUD2/BQLJyoplM21AaMBTlOVKVK7WiirVIyw6qSP8ttedTsmrzUjJ6jZA8GPBSXpIzdivEwCsfMkXu+ODHZIzlHHXQ4IR6DdadmRlcGO9J+K5gx978lT4AdpU/KqSrq85PQZfGj2D1ykqW/SdFhwENxTT3jcbS01iE9NSdiQ2Qj1PMdKbmpqkMvJxthh2rPozXPm7I7uhvPTbsCN/VYPMoeXFaQ8gplgAUA7Sz7RTILAx6Ke7EEPZGmvVB+przpaE2bEY9P5mZRZ/4UmxYBjsY56obecbtHTgqa8kEZ4NpROhpq4EFlukIx+3pVBvws5XEnBjzkSXq7pesNdtR/S9Ve6veAxA58KHZ6Axmnq94isavUqaFzo2ZwUT1lnOaDkBlTSkRaR5cz/oNLK/+IVSV/jvq3yHzslk6eonzCVD7hSTOeK2c+j1QqFOmGEurGlMjz7FRPGRcUaLIhp3XcUo0lHW/ltZf+dQNKBlWYOl1GKP69vpClOWY+fEQKduKx+jbRMOAhT1COTaKkDHDU71spkYMeSjzqYKdyZTnquvtR193vmmtBK3/QK1ywIwV8yuB+9zdZ2P1NVlS/RdZhwEOeE2mAwvr8JM2XmZQTFSYirYlfyTxurc5ycl6vUIFVLIGOej1a1Nd5NBOrkj2ShJ4Jr1wsEAggPT0ddXV1SEtLczo55EJavSqkbqtS5qR+QgViaxSqvMkn6kBkyhsBi/vNEw+ThQL2zm3n5Dx6ci8tjSlw9F77Tk2F4zS7798MeCghXVr5RwDAwTWny++ZdVPWKtFIxMwMQFB3dQY95mDAE/63zJ7fShm4R1qvupSJHRjCs/v+zV5alJD+X+52AMDf8HPAE83gaXqra8zuLRIvzOyurqzGcUOXbKfEQ7CjZGb1W8GMuWi3R4S8dsyePFh93hpZbyJc3/GGJTyUkB7+/AoAwLPrL4Z/r8/QjOyA8XYpWuuS1pEopT/RNF4NV5rhlsawdoqX0h2rqKtJlUGFVlAdy7UVKkgPF3CRMSzhIbLBb9I+xT8D/eU2PPX5SS3a9Zg1XH6kdSTKpIOhBojU6lmn58Zu1ijb8SLRgx01qWOA2ddNuNJIBjvxjQEPJawVNT0sn/iP7VaC6blZGLmhaC3r1SCIwc7JhwKtaqZ2ewSqnxtvec9IBjvxjd3SKeFcWvlH3PLl74MaLEuUXUrDBSuRPgvXLV7POih6XrwpeXGboqVVolOfnxQU6EZ7bSXyUBKJwBUBz7x589ClSxe0bt0aRUVFWLdundNJIo/SytCk0h29wY6ZGPRQJG4dc8dJoaqx1A8aegOYghlzLQt2vFriGI8cD3heeeUVjB8/HtOmTcOnn36Kvn37orS0FIcOHXI6aeRRB9ecLpfuRJrhO9QghVL7HnXAEk0AI32HT5fxI/3rBvll5W+sf248q7JCCFXSoxYq4JCCHL0zq7fbIyIGL6FGdid3cDzgmTNnDsaOHYsbb7wRPXv2xFNPPYXU1FQ8//zzTieNPKZwzBw0dG6UGydL/ypFM0Kw3iossk+8j+7MBsqRqRssS8dc+UCih56RkbXWpQ5uwgU6DILcwdFGy42NjdiwYQPKy3++sJOTkzF48GBUVVVpfqehoQENDT8/VQUCAcvTSfFPGgDv5y7oLZfRm0FaEdhI6ywcM4ftNWJUOGYO2ln8G1aOA8RgRx/lpKVaAa5ZvSy1lAyqAP57DugJZnhNu4OjAc+3336LpqYm5OTkBL2fk5OD7du3a36noqIC06dPtyN55BHKzEmZAWpNKxGJHaU4idJN3cvWPzceJYMqWgRGkW7CDHaMWx+md5bZgYa0Pr3HSM/ExQyG7BN33dLLy8sxfvzPJ0ggEEBeXp6DKaJ4YbQLuvrJ0axgJ/3rhpAlBMrfk7rbSpgxxhetY8xgxxpa3dUBa+aoCjdJqV5a17k0rhSvc+s4GvBkZmYiJSUFtbW1Qe/X1tYiNzdX8zt+vx9+f+IOK0/mcqLreF13f8igJ1xQxswwfihLFfVisBOdSEGNcj435XKhgiQt6usu3PxwUsmeepZ25STGkUqknJwM1cscbbTs8/kwYMAAVFZWyu81NzejsrISxcXFDqaMyFrRtgFh48fI3HCDMHp82fU8epFKcJQ9KtXXT7ju7VoKZswNGpVdaiCtPOf0BK7K31WWJEs9x5QPPOz5ZR7Hq7TGjx+PkSNHorCwEOeffz4ee+wx/Pjjj7jxxhudThp5iLJEJR56U4VLo1bmx+qQ+MVjZw4jJTaRhKv21urdqaZn2hP1NR4p7bG2/WEJsUsmD33yyScxe/Zs1NTUoF+/fnj88cdRVFSk67ucPJQikYqYw7WdiXfKEgLePE/SajTsRol+EzJbqMDBSFseZTWY+rsFM+aioXMjdo+cJP+W1hxbegIU5TJG2hZGWm+o33HbuWb3/dsVAU8sGPBQOMqbXqIEPACDHonbgx633YC8wuyZ07XWry5RimZiUanURR1gKalLlKShNQBotk0KlV49y9nN7vu34wMPElktEdtHlAyqkF+JrHJlueXHP9r1J+J5aRetG7tZ7WCkEh7l70RbTS4FSOufG4/qKeNCBiRdzviP/AJ+bjskCbdtXRY/qqsaLhEw4CFPU5d0KKcEsHpqALt4YRsSiXTesRTOWurgQT3BaCyUozNLvxNLaZ0yXerAx7/Xh93fZAGA/G+odUglOdKUGcppM9TVcok4lY3jjZaJ7BAuKIj3qi6pfZLW+3SSlcc42vWWDKpg0GMx6SYv3fgbOres7tIqnQkXvFRPGWfL4IHKKjP/Xh92IyvsNBjqNkfSNu8eOSnsuq0Yq8itWMJDnqfnphJNaY+bSorquvsZ4ITgtqCCx8l+ypu+ci69hs6NhiYclcQa4OgJmNSBWaQ5v9SB2+6RkzSDnUTGgIcSgm/7N7qWczpwiRUDH20Memj3yEnw7/XBv9cnBz1SECG1iVG+Yqn+CteGLpr1GpmgWG9VlbqKKxGwSosSwrKD81pkPsqbTrwHOmq8obYU71WXFLtIjZnVpT2hgpNQXdaB4GEwtOhtvxUquNGabsbsgQnd2o09VuyWTgklVNCjzJyiHSXX6ZtpqBu61zKtaLmxizqPTfwJNe2DOm8xu1QxUjWYsmt7LHPw2Rns2H3/ZgkPJTT1U1g0N0S33ESVT5VSmnhD/ZlbjpMSR7/1JiuqUCOdJ1qjO+sdzFCLW8fuiQVLeCjhaNWru/FmGA2OuByaG0t4AE4tQdaItaTGjoCHJTxEFDPeQOMHj1XiUJbASFXQRgNevYFMrCWHXirZkbCXFiUcrczF7K7lTjSCln6TN9D4YmQ0bI6e7R1SaWNdd788I3rhmDlhjy9nTY8NAx4ihWgDHzPaAkXLDeMAuZ1bq7OM4rAD3qf3+DLwNY4BDyUks0tBeBNyt3g/PiWDKoKe7vmk737Rlsbx4cU6DHiI8POTc7gnaD0ZkdMjL7M6qyU+CZOdpEBHykuk88/sIFWqCiP9GPBQwtIbHEQKYJwOcsjbvFIdl0iUx0sKTPTmD0YfWpwKeuIx2GLAQwlNylxCBSzq99TLuSHIYbuO8KIdSNINGOzEn1DHS+9xjMdAIl4w4KGEp3yiUpfWaGVS6qe3UNx04yRtbii903qil3rsMNiJH+p2VrHQ6q3lxqrZeAvOGPAQ4eRNx0gVl152VnWx/Y4x6pI65bHSc9zMOK5aY6XoubFxdGb3MDPQUZKqwqTzIVzw68Tkn7Gcg05NVmpZwLN7926MHj0aXbt2RZs2bdC9e3dMmzYNjY2NQctt3rwZF198MVq3bo28vDzMmjXLqiQRRaQMfMLd9Iw++bO0J35EKtUL955e658bH/KGEWm9DHbcRTmejlXrd2tJSjTnohTsOBH0WDbS8vbt29Hc3Iynn34aZ555JrZu3YqxY8fixx9/xJ///GcAJ4eVHjJkCAYPHoynnnoKW7ZswU033YT27dvj5ptvtippRBFVriwP+6StNekoeUOkiWRjmXVdzw2Cs7rHj5JBFYCDx0o6V0LNrE7BLAt4hg4diqFDh8p/d+vWDdXV1ViwYIEc8Lz44otobGzE888/D5/Ph169emHTpk2YM2cOAx5ytWgHJ+SNzH7K/a78v3Ky1VjWaeQ7elSuLHftEz0Fc8P1LJ2L0gOa26u2lVNWBAIBW3/b1jY8dXV16NChg/x3VVUVBg4cCJ/PJ79XWlqK6upq/PDDD5rraGhoQCAQCHoRWUFvxsFeUu5WubI8qBG63sBD7zHVOz6T229EpI80zk6koNTq9nuh1u3Gxs1uYVvAs2PHDjzxxBO45ZZb5PdqamqQk5MTtJz0d01NjeZ6KioqkJ6eLr/y8vKsSzRRjPQMaGgGtusIz4pgQ28jZ6PBDm9Y7qMMcvRcy3ZUdSvTof49K86hwjFzHGtsbBbDAc/kyZORlJQU9rV9+/ag7+zfvx9Dhw7Fb3/7W4wdOzamBJeXl6Ourk5+7du3L6b1EZnBybY8DHb00RpqINKwAkaPqxlP9SwtdA/lqMlGWHkM9Z5fVgXO8Rz0GG7DM2HCBIwaNSrsMt26dZP/f+DAAQwaNAgXXHABnnnmmaDlcnNzUVtbG/Se9Hdubq7muv1+P/x+ZgjkHqEyIN643Eer7Y3y73CDTxo5nuq2QnqVDKpAepjvMLi1hzpYUJbkRTpf7KBOhx2dKNY/Nz6oh1W7PSLuzkfDAU9WVhaysrJ0Lbt//34MGjQIAwYMwMKFC5GcHFygVFxcjPvuuw/Hjx9Hq1atAAArVqxAQUEBMjIyjCaNKOGwl5h+Us+7cA2OI7XxibS/zQhyGSg7K1TJSLRBRTQBc6R16V1vyaAKU6tzpQbH8dqoPkkIYUl/tv379+PSSy9Ffn4+Fi9ejJSUFPkzqfSmrq4OBQUFGDJkCCZNmoStW7fipptuwty5c3X30goEAkhPT0ddXR3S0tKs2BRKUJGKsu24+anF2xOVG0UayC1SiV24425GyUyomwmPvT2U171WcKzu9adHrHmBkXNOa1m3Npi3+/5tWbf0FStWYMeOHdixYwfOOOOMoM+kGCs9PR3vvPMOysrKMGDAAGRmZmLq1Knskk5xj8FOYuLQA94SKpiIppTHqvPCiZKeeGVZwDNq1KiIbX0AoE+fPvjggw+sSgZRTCJlJrGM5WIEgx1zhRpQkCgcve139JS6GP3dWKc6iZdxeqxkWcBD5HVajVLNvmky0LGHkeOmdfPRquKIduwdrdIAngf2ClUiY9bAlUZ/24zfsevhzM0Y8BCFEa6dB8fViW+xVEuEunko34/2STqRn8DdJFKPPiOiHZXbrCBF+v1EP7c4WzpRCOEyKSkj0nrSZxsOd1OOvGxUoj8hJwpliV00jdTDDUIZ6n2t3zIz2CGW8BBpijQeCiJ8Rt7FoMf7jJTghmvYrFVCFKlk0EqFY+YkdOkxS3iIQmCdN4UTqjSvrrs/4asO4lmsx045lYyy5Cba0qJQ6yfjGPAQ6WD1RIBkL9/2b6L6nta8aAyMyQl6gh4GRsEY8BBFYOZIqXokcpGzXZYdnCf/34wnZuX3efzin55SHj1j8uidODjcA5XeHlt2TFIc7xjwEKlI41U4MXEkb5b2Ud/U9NwoYh0LhbzDaMmeMiDRG5iEaxStfBDTaivEwKclBjxEGiJlSurghjc6b1OeC6GOe7Tj7pA76QluY520M1wewyor8zHgIYqBWW172EbIGVIXdSNVCqE+Y7DjLerjGWqwyVCfm82MKqtEL0Fmt3QilcqV5SgcM8e2eZFYMuAs6XgD2jct9YCDWhL9RuJV6jwg2lKfUNd3pJnZI/0m524zhgEPkYreMXjMKtlhsOMeyuMabpZs5Xs8fqQ1UKGe86JyZXmLoCfSYIbKLu8MdoxhlRaRQ1iF5U7hxteRSDc2BjveVTKoIqqAonJluaHzQmvZSKU6gPH2OyyFZAkPkSY946yYUcrDG6Y7hKu64DEiwJx50kJRlvRotRWKtTSHwc5JSUII4XQiYhEIBJCeno66ujqkpaU5nRzyAKk9h0SZARkZEyPScryRuouyaoHHhoCWpTxWl+rprd4ywslgR8pLQ6XB7vs3S3iIFEoGVQBhMplou6LyBup+PEakZvc5odWmh8zDNjxECkaeqMItq+7qzEyMiIzySlWUutTcKbYEPA0NDejXrx+SkpKwadOmoM82b96Miy++GK1bt0ZeXh5mzZplR5KIWjDjotQaTZWNk4lIL6nRs1S6FM/5hzJgc0PQY0vA86c//QmdOnVq8X4gEMCQIUOQn5+PDRs2YPbs2XjggQfwzDPP2JEsoqgoS2/0DATG2bOJKFrxnneogx7p5QTL2/AsW7YM77zzDv71r39h2bJlQZ+9+OKLaGxsxPPPPw+fz4devXph06ZNmDNnDm6++Wark0YUNa25a0KJ5yc0InLe+ufG6w4S3FgNVp+fhHZ7nO8fZWnAU1tbi7Fjx+L1119Hampqi8+rqqowcOBA+Hw++b3S0lI8+uij+OGHH5CRkWFl8ohMJZX4uDHDIaL4Fi5fidQbyimFY+agPj8JgDuCHssCHiEERo0ahVtvvRWFhYXYvXt3i2VqamrQtWvXoPdycnLkz7QCnoaGBjQ0/PzEHAgEzE04kYqRdjhuy3CIyPukfEfqHOGWarBQJVNO5ZOG2/BMnjwZSUlJYV/bt2/HE088gfr6epSXm7vjKyoqkJ6eLr/y8vJMXT+RHqymIiKnqdvExDq5qFkKZsxFwYy5AFoGN04+FBou4ZkwYQJGjRoVdplu3brhvffeQ1VVFfz+4J1fWFiIESNGYPHixcjNzUVtbW3Q59Lfubm5musuLy/H+PE/77BAIMCghyynLuXRKvVxy1MVESUeN/SCUnNbmgwHPFlZWcjKyoq43OOPP46HHnpI/vvAgQMoLS3FK6+8gqKiIgBAcXEx7rvvPhw/fhytWrUCAKxYsQIFBQUh2+/4/f4WQRRRrCJdmOrgxqoh5omIjJDayDjdPkYileyo0+OG6n7L2vB07tw56O+2bdsCALp3744zzjgDAPD73/8e06dPx+jRozFp0iRs3boVf/nLXzB37lyrkkVkWLjqK7fVmRNR4lj/3Hg5wHADrbS4IdCRODq1RHp6Ot555x2UlZVhwIAByMzMxNSpU9klnVyHE0sSkRu12yPkUh7AmQCjy+JH4d/ra/G+m4IdwMaAp0uXLtCap7RPnz744IMP7EoGERGRZyh7QjkV7Ci5pWpNCycPJdJBGmOHpTtE5DZOlur4cbJkRxnouK1kR8KAhxKe3p4EDHaIiE6SqrCUgU761w2uzhsZ8BBpCNdQ2c0XNBGRXdxcfaXFlslDidysPj9JfkncMHgXEZFbVU8Z1+I9tz8MsoSHEl5D50YA0OxloCS14WFXdCIi97bVCYUlPJTQpHEj9AQ7REQUv1jCQwkvUrADtGzTw9IdIqL4woCHyAAGOkRE8YlVWkREROR5LOGhhFY9ZZyuuWiMluxIDZuj+S4REZmPJTxEISgbKisDmEikZeu6+1HX3W/ou0REZA2W8BAp1OcnmTaYltTQmSU8RETOY8BDCS9UtVa40ZbDYYBDROQ+rNIi0olVU0RE8StJCBFfk2GoBAIBpKeno66uDmlpaU4nh+JcwYy5mlVaHIeHiMhcdt+/WcJDpFA9ZZzmcOkcaZmIKL6xDQ+RBnXQUzhmDgCW7BARxSsGPEQ6xNskeUREFMzSKq0333wTRUVFaNOmDTIyMnDllVcGfb53714MGzYMqampyM7OxsSJE3HixAkrk0REREQJyLISnn/9618YO3YsHnnkEVx22WU4ceIEtm7dKn/e1NSEYcOGITc3F2vWrMHBgwdxww03oFWrVnjkkUesShYRERElIEt6aZ04cQJdunTB9OnTMXr0aM1lli1bhiuuuAIHDhxATk4OAOCpp57CpEmT8J///Ac+X+QZrAH20iIiIopHnuil9emnn2L//v1ITk7Geeedh44dO+Lyyy8PKuGpqqpC79695WAHAEpLSxEIBPD555+HXHdDQwMCgUDQi4iIiCgcSwKenTt3AgAeeOAB3H///Vi6dCkyMjJw6aWX4vvvvwcA1NTUBAU7AOS/a2pqQq67oqIC6enp8isvL8+KTSAiIiIPMRTwTJ48GUlJSWFf27dvR3NzMwDgvvvuw9VXX40BAwZg4cKFSEpKwquvvhpTgsvLy1FXVye/9u3bF9P6iIiIyPsMNVqeMGECRo0aFXaZbt264eDBgwCAnj17yu/7/X5069YNe/fuBQDk5uZi3bp1Qd+tra2VPwvF7/fD7+cgcERERKSfoYAnKysLWVlZEZcbMGAA/H4/qqurcdFFFwEAjh8/jt27dyM/Px8AUFxcjIcffhiHDh1CdnY2AGDFihVIS0sLCpSIiIiIYmVJt/S0tDTceuutmDZtGvLy8pCfn4/Zs2cDAH77298CAIYMGYKePXvi+uuvx6xZs1BTU4P7778fZWVlLMEhIiIiU1k2Ds/s2bNxyimn4Prrr8dPP/2EoqIivPfee8jIyAAApKSkYOnSpbjttttQXFyMU089FSNHjsSDDz5oVZKIiIgoQXG2dCIiIrKdJ8bhISIiInITBjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeZYFPF9++SWGDx+OzMxMpKWl4aKLLsLKlSuDltm7dy+GDRuG1NRUZGdnY+LEiThx4oRVSSIiIqIEZVnAc8UVV+DEiRN47733sGHDBvTt2xdXXHEFampqAABNTU0YNmwYGhsbsWbNGixevBiLFi3C1KlTrUoSERERJagkIYQwe6XffvstsrKy8P777+Piiy8GANTX1yMtLQ0rVqzA4MGDsWzZMlxxxRU4cOAAcnJyAABPPfUUJk2ahP/85z/w+Xy6fisQCCA9PR11dXVIS0sze1OIiIjIAnbfvy0p4TnttNNQUFCAv/3tb/jxxx9x4sQJPP3008jOzsaAAQMAAFVVVejdu7cc7ABAaWkpAoEAPv/885DrbmhoQCAQCHoRERERhXOKFStNSkrCu+++iyuvvBLt2rVDcnIysrOzsXz5cmRkZAAAampqgoIdAPLfUrWXloqKCkyfPt2KZBMREZFHGSrhmTx5MpKSksK+tm/fDiEEysrKkJ2djQ8++ADr1q3DlVdeiV/96lc4ePBgTAkuLy9HXV2d/Nq3b19M6yMiIiLvM1TCM2HCBIwaNSrsMt26dcN7772HpUuX4ocffpDr5ebPn48VK1Zg8eLFmDx5MnJzc7Fu3bqg79bW1gIAcnNzQ67f7/fD7/cbSTYRERElOEMBT1ZWFrKysiIud/ToUQBAcnJwAVJycjKam5sBAMXFxXj44Ydx6NAhZGdnAwBWrFiBtLQ09OzZ00iyiIiIiMKypNFycXExMjIyMHLkSHz22Wf48ssvMXHiROzatQvDhg0DAAwZMgQ9e/bE9ddfj88++wxvv/027r//fpSVlbEEh4iIiExlScCTmZmJ5cuX48iRI7jssstQWFiIDz/8EG+88Qb69u0LAEhJScHSpUuRkpKC4uJi/OEPf8ANN9yABx980IokERERUQKzZBweO3EcHiIiovjjiXF4iIiIiNyEAQ8RERF5HgMeIiIi8jwGPEREROR5DHiIiIjI8xjwEBERkedZMnmonaRe9Zw1nYiIKH5I9227RseJ+4Cnvr4eAJCXl+dwSoiIiMio+vp6pKenW/47cT/wYHNzMw4cOIB27dohKSnJ6eRoCgQCyMvLw759+zg44n9xn7TEfdIS90lL3CctcZ+0FA/7RAiB+vp6dOrUqcXcm1aI+xKe5ORknHHGGU4nQ5e0tDTXnnhO4T5pifukJe6TlrhPWuI+acnt+8SOkh0JGy0TERGR5zHgISIiIs9jwGMDv9+PadOmwe/3O50U1+A+aYn7pCXuk5a4T1riPmmJ+6SluG+0TERERBQJS3iIiIjI8xjwEBERkecx4CEiIiLPY8BDREREnseAx2QPP/wwLrjgAqSmpqJ9+/aayyQlJbV4LVmyJGiZVatWoX///vD7/TjzzDOxaNEi6xNvET37ZO/evRg2bBhSU1ORnZ2NiRMn4sSJE0HLeGmfqHXp0qXFOTFz5sygZTZv3oyLL74YrVu3Rl5eHmbNmuVQau0zb948dOnSBa1bt0ZRURHWrVvndJJs8cADD7Q4H3r06CF/fuzYMZSVleG0005D27ZtcfXVV6O2ttbBFJvv/fffx69+9St06tQJSUlJeP3114M+F0Jg6tSp6NixI9q0aYPBgwfjq6++Clrm+++/x4gRI5CWlob27dtj9OjROHLkiI1bYa5I+2TUqFEtzpuhQ4cGLeO1fWIEAx6TNTY24re//S1uu+22sMstXLgQBw8elF9XXnml/NmuXbswbNgwDBo0CJs2bcI999yDMWPG4O2337Y49daItE+ampowbNgwNDY2Ys2aNVi8eDEWLVqEqVOnyst4bZ9oefDBB4POiTvvvFP+LBAIYMiQIcjPz8eGDRswe/ZsPPDAA3jmmWccTLG1XnnlFYwfPx7Tpk3Dp59+ir59+6K0tBSHDh1yOmm26NWrV9D58OGHH8qfjRs3Dv/3f/+HV199FatXr8aBAwdw1VVXOZha8/3444/o27cv5s2bp/n5rFmz8Pjjj+Opp57C2rVrceqpp6K0tBTHjh2TlxkxYgQ+//xzrFixAkuXLsX777+Pm2++2a5NMF2kfQIAQ4cODTpvXn755aDPvbZPDBFkiYULF4r09HTNzwCI1157LeR3//SnP4levXoFvXfNNdeI0tJSE1Nov1D75K233hLJycmipqZGfm/BggUiLS1NNDQ0CCG8u08k+fn5Yu7cuSE/nz9/vsjIyJD3hxBCTJo0SRQUFNiQOmecf/75oqysTP67qalJdOrUSVRUVDiYKntMmzZN9O3bV/Ozw4cPi1atWolXX31Vfu+LL74QAERVVZVNKbSXOs9sbm4Wubm5Yvbs2fJ7hw8fFn6/X7z88stCCCG2bdsmAIhPPvlEXmbZsmUiKSlJ7N+/37a0W0XrPjJy5EgxfPjwkN/x+j6JhCU8DikrK0NmZibOP/98PP/88xCK4ZCqqqowePDgoOVLS0tRVVVldzJtUVVVhd69eyMnJ0d+r7S0FIFAAJ9//rm8jNf3ycyZM3HaaafhvPPOw+zZs4Oq9KqqqjBw4ED4fD75vdLSUlRXV+OHH35wIrmWamxsxIYNG4KOeXJyMgYPHuypYx7OV199hU6dOqFbt24YMWIE9u7dCwDYsGEDjh8/HrRvevTogc6dOyfMvtm1axdqamqC9kF6ejqKiorkfVBVVYX27dujsLBQXmbw4MFITk7G2rVrbU+zXVatWoXs7GwUFBTgtttuw3fffSd/lqj7RBL3k4fGowcffBCXXXYZUlNT8c477+D222/HkSNHcNdddwEAampqgm7+AJCTk4NAIICffvoJbdq0cSLZlgm1vdJn4Zbxyj6566670L9/f3To0AFr1qxBeXk5Dh48iDlz5gA4uf1du3YN+o5yH2VkZNieZit9++23aGpq0jzm27dvdyhV9ikqKsKiRYtQUFCAgwcPYvr06bj44ouxdetW1NTUwOfztWgPl5OTI18vXidtp9b5ocwzsrOzgz4/5ZRT0KFDB8/up6FDh+Kqq65C165d8fXXX+Pee+/F5ZdfjqqqKqSkpCTkPlFiwKPD5MmT8eijj4Zd5osvvghqVBjOlClT5P+fd955+PHHHzF79mw54IkHZu8TLzKyj8aPHy+/16dPH/h8Ptxyyy2oqKjg0PAJ6PLLL5f/36dPHxQVFSE/Px//+Mc/4j64J+tce+218v979+6NPn36oHv37li1ahVKSkocTJk7MODRYcKECRg1alTYZbp16xb1+ouKijBjxgw0NDTA7/cjNze3RY+L2tpapKWluSazM3Of5Obmtuh9I21/bm6u/K/b94laLPuoqKgIJ06cwO7du1FQUBBy+4Gf95GXZGZmIiUlRXObvbi9kbRv3x5nn302duzYgf/3//4fGhsbcfjw4aBSnkTaN9J21tbWomPHjvL7tbW16Nevn7yMuoH7iRMn8P333yfMfurWrRsyMzOxY8cOlJSUJPw+YcCjQ1ZWFrKysixb/6ZNm5CRkSE/yRcXF+Ott94KWmbFihUoLi62LA1GmblPiouL8fDDD+PQoUNyceuKFSuQlpaGnj17ysu4fZ+oxbKPNm3ahOTkZHl/FBcX47777sPx48fRqlUrACe3v6CgwHPVWQDg8/kwYMAAVFZWyj0Ym5ubUVlZiTvuuMPZxDngyJEj+Prrr3H99ddjwIABaNWqFSorK3H11VcDAKqrq7F3715XXw9m6tq1K3Jzc1FZWSkHOIFAAGvXrpV7gxYXF+Pw4cPYsGEDBgwYAAB477330NzcjKKiIqeSbqtvvvkG3333nRwUJvw+cbrVtNfs2bNHbNy4UUyfPl20bdtWbNy4UWzcuFHU19cLIYT497//LZ599lmxZcsW8dVXX4n58+eL1NRUMXXqVHkdO3fuFKmpqWLixIniiy++EPPmzRMpKSli+fLlTm1WTCLtkxMnTohzzz1XDBkyRGzatEksX75cZGVlifLycnkdXtsnSmvWrBFz584VmzZtEl9//bV44YUXRFZWlrjhhhvkZQ4fPixycnLE9ddfL7Zu3SqWLFkiUlNTxdNPP+1gyq21ZMkS4ff7xaJFi8S2bdvEzTffLNq3bx/Um8+rJkyYIFatWiV27dolPvroIzF48GCRmZkpDh06JIQQ4tZbbxWdO3cW7733nli/fr0oLi4WxcXFDqfaXPX19XJeAUDMmTNHbNy4UezZs0cIIcTMmTNF+/btxRtvvCE2b94shg8fLrp27Sp++ukneR1Dhw4V5513nli7dq348MMPxVlnnSWuu+46pzYpZuH2SX19vfjjH/8oqqqqxK5du8S7774r+vfvL8466yxx7NgxeR1e2ydGMOAx2ciRIwWAFq+VK1cKIU52AezXr59o27atOPXUU0Xfvn3FU089JZqamoLWs3LlStGvXz/h8/lEt27dxMKFC+3fGJNE2idCCLF7925x+eWXizZt2ojMzEwxYcIEcfz48aD1eGmfKG3YsEEUFRWJ9PR00bp1a3HOOeeIRx55JCiTEkKIzz77TFx00UXC7/eL008/XcycOdOhFNvniSeeEJ07dxY+n0+cf/754uOPP3Y6Sba45pprRMeOHYXP5xOnn366uOaaa8SOHTvkz3/66Sdx++23i4yMDJGamip+/etfi4MHDzqYYvOtXLlSM98YOXKkEOJk1/QpU6aInJwc4ff7RUlJiaiurg5ax3fffSeuu+460bZtW5GWliZuvPFG+UErHoXbJ0ePHhVDhgwRWVlZolWrViI/P1+MHTu2xQOC1/aJEUlCKPpDExEREXkQx+EhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIizzvF6QSY4dixY2hsbHQ6GURERGSAz+dD69atbfmtuA94jh07hvannoaG5qNOJ4WIiIgMyM3Nxa5du2wJeuI+4GlsbERD81FcmnUDTknyOZ0cWzWe3cnpJDgi0NXvdBIcUZ+X5HQSHNGYl5ilt507fet0EhwxKOdLp5PgiCvbfeZ0Emx35IjApUU1aGxsZMBjxClJPpySnFgBT/Mp9hQDuk2KLzEDnpTWiRnwJLdJzKaGp5yamOd567atnE6CI9q2S8TzvNnWX0vEPUxEREQJhgEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPMY8BAREZHnMeAhIiIiz2PAQ0RERJ7HgIeIiIg8jwEPEREReR4DHiIiIvI8BjxERETkeQx4iIiIyPNOcToBZjkhGoFmp1NhrxMnjjmdBEc0NQqnk+CIpmNJTifBEc0/NTqdBEec+LHB6SQ44tiR404nwRFHkhLsBgbgyBF78/IkIURc3z3q6upwxhln4MiRI04nhYiIiAxo27YtvvnmG6Snp1v+W3FfwpOUlIQjR45g3759SEtLczo5tgkEAsjLy+N2JwhuN7c7EXC7E3O7k5LsKb2O+4BHkpaWllAnioTbnVi43YmF251YEnW77cJGy0REROR5DHiIiIjI8+I+4PH7/Zg2bRr8fr/TSbEVt5vbnQi43dzuRMDttme7476XFhEREVEkcV/CQ0RERBQJAx4iIiLyPAY8RERE5HkMeIiIiMjzXBvw/M///A86d+6M1q1bo2PHjrj++utx4MCBoGU2b96Miy++GK1bt0ZeXh5mzZrVYj2vvvoqevTogdatW6N379546623gj4XQmDq1Kno2LEj2rRpg8GDB+Orr76ydNvCefjhh3HBBRcgNTUV7du311wmKSmpxWvJkiVBy6xatQr9+/eH3+/HmWeeiUWLFrVYz7x589ClSxe0bt0aRUVFWLdunQVbZJ54S6/a+++/j1/96lfo1KkTkpKS8Prrrwd9PmrUqBbHdejQoUHLfP/99xgxYgTS0tLQvn17jB49usW0KnquCzstWLAAffr0kQdVKy4uxrJly+TPjx07hrKyMpx22mlo27Ytrr76atTW1gatY+/evRg2bBhSU1ORnZ2NiRMn4sSJE0HL6DnnnTJz5kwkJSXhnnvukd+79NJLWxzvW2+9Neh78bjd+/fvxx/+8AecdtppaNOmDXr37o3169fLn+vJc+PxPO/SpYtm3lxWVgbAu8e7vr4e99xzD/Lz89GmTRtccMEF+OSTT+TPXXW8hUvNmTNHVFVVid27d4uPPvpIFBcXi+LiYvnzuro6kZOTI0aMGCG2bt0qXn75ZdGmTRvx9NNPy8t89NFHIiUlRcyaNUts27ZN3H///aJVq1Ziy5Yt8jIzZ84U6enp4vXXXxefffaZ+J//+R/RtWtX8dNPP9m6vZKpU6eKOXPmiPHjx4v09HTNZQCIhQsXioMHD8ovZXp37twpUlNTxfjx48W2bdvEE088IVJSUsTy5cvlZZYsWSJ8Pp94/vnnxeeffy7Gjh0r2rdvL2pra63exKjEW3q1vPXWW+K+++4T//u//ysAiNdeey3o85EjR4qhQ4cGHdfvv/8+aJmhQ4eKvn37io8//lh88MEH4swzzxTXXXed/Lme68Ju//73v8Wbb74pvvzyS1FdXS3uvfde0apVK7F161YhhBC33nqryMvLE5WVlWL9+vXil7/8pbjgggvk7584cUKce+65YvDgwWLjxo3irbfeEpmZmaK8vFxeRs8575R169aJLl26iD59+oi7775bfv+SSy4RY8eODTredXV18ufxuN3ff/+9yM/PF6NGjRJr164VO3fuFG+//bbYsWOHvIyePDcez/NDhw4FHcsVK1YIAGLlypVCCG8ebyGE+N3vfid69uwpVq9eLb766isxbdo0kZaWJr755hshhLuOt2sDHrU33nhDJCUlicbGRiGEEPPnzxcZGRmioaFBXmbSpEmioKBA/vt3v/udGDZsWNB6ioqKxC233CKEEKK5uVnk5uaK2bNny58fPnxY+P1+8fLLL1u5OREtXLgwbMCjvlkq/elPfxK9evUKeu+aa64RpaWl8t/nn3++KCsrk/9uamoSnTp1EhUVFTGl2yrxlt5IQgU8w4cPD/mdbdu2CQDik08+kd9btmyZSEpKEvv37xdC6Lsu3CAjI0M899xz4vDhw6JVq1bi1VdflT/74osvBABRVVUlhDgZKCYnJ4uamhp5mQULFoi0tDR5O/Wc806or68XZ511llixYoW45JJLWgQ8yr/V4nG7J02aJC666KKQn+vJc71ynt99992ie/fuorm5WQjhzeN99OhRkZKSIpYuXRr0fv/+/cV9993nuuPt2iotpe+//x4vvvgiLrjgArRq1QoAUFVVhYEDB8Ln88nLlZaWorq6Gj/88IO8zODBg4PWVVpaiqqqKgDArl27UFNTE7RMeno6ioqK5GXcqqysDJmZmTj//PPx/PPPQyiGU4q03Y2NjdiwYUPQMsnJyRg8eLArtzve0huLVatWITs7GwUFBbjtttvw3XffyZ9VVVWhffv2KCwslN8bPHgwkpOTsXbtWnmZSNeFk5qamrBkyRL8+OOPKC4uxoYNG3D8+PGgY9ujRw907txZPrZVVVXo3bs3cnJy5GVKS0sRCATw+eefy8uEO+edUlZWhmHDhrVIm+TFF19EZmYmzj33XJSXl+Po0aPyZ/G43f/+979RWFiI3/72t8jOzsZ5552HZ599Vv5cT57rhfO8sbERL7zwAm666aagiTG9drxPnDiBpqYmtG7dOuj9Nm3a4MMPP3Td8Xb15KGTJk3Ck08+iaNHj+KXv/wlli5dKn9WU1ODrl27Bi0vnSg1NTXIyMhATU1N0MkjLVNTUyMvp/ye1jJu9OCDD+Kyyy5Damoq3nnnHdx+++04cuQI7rrrLgAIud2BQAA//fQTfvjhBzQ1NWkus337dtu2Q69vv/02rtIbraFDh+Kqq65C165d8fXXX+Pee+/F5ZdfjqqqKqSkpKCmpgbZ2dlB3znllFPQoUOHoHM60nXhhC1btqC4uBjHjh1D27Zt8dprr6Fnz57YtGkTfD5fi/Zq6utU69hLn4VbRjrn27RpY9GWhbZkyRJ8+umnQe0ZlH7/+98jPz8fnTp1wubNmzFp0iRUV1fjf//3fwHE53bv3LkTCxYswPjx43Hvvffik08+wV133QWfz4eRI0fqynPj+TyXvP766zh8+DBGjRolv+fF492uXTsUFxdjxowZOOecc5CTk4OXX34ZVVVVOPPMM113vG0NeCZPnoxHH3007DJffPEFevToAQCYOHEiRo8ejT179mD69Om44YYbsHTpUtumkjeL0e2OZMqUKfL/zzvvPPz444+YPXu2HPBQfLr22mvl//fu3Rt9+vRB9+7dsWrVKpSUlDiYstgVFBRg06ZNqKurwz//+U+MHDkSq1evdjpZltm3bx/uvvturFixosXTr+Tmm2+W/9+7d2907NgRJSUl+Prrr9G9e3e7kmqq5uZmFBYW4pFHHgFwMn/aunUrnnrqKYwcOdLh1Nnnr3/9Ky6//HJ06tRJfs+LxxsA/v73v+Omm27C6aefjpSUFPTv3x/XXXcdNmzY4HTSWrA14JkwYUJQxKulW7du8v8zMzORmZmJs88+G+eccw7y8vLw8ccfo7i4GLm5uS16ckh/5+bmyv9qLaP8XHqvY8eOQcv069cvqm3UYnS7jSoqKsKMGTPQ0NAAv98fcrvT0tLQpk0bpKSkICUlJey+cZPMzMy4Sq9ZunXrhszMTOzYsQMlJSXIzc3FoUOHgpY5ceIEvv/++4jnvPSZU3w+H84880wAwIABA/DJJ5/gL3/5C6655ho0Njbi8OHDQaU86utU3SNP77UunfN227BhAw4dOoT+/fvL7zU1NeH999/Hk08+iYaGBqSkpAR9p6ioCACwY8cOdO/ePS63u2PHjujZs2fQe+eccw7+9a9/AdCX58bzeQ4Ae/bswbvvviuX3ITiheMNAN27d8fq1avx448/IhAIoGPHjrjmmmvQrVs31x1vW9vwZGVloUePHmFfyjo6pebmZgBAQ0MDAKC4uBjvv/8+jh8/Li+zYsUKFBQUyMVbxcXFqKysDFrPihUrUFxcDADo2rUrcnNzg5YJBAJYu3atvIzT263Hpk2bkJGRIU/AFmm7fT4fBgwYELRMc3MzKisrTd1us8Rbes3yzTff4LvvvpMziuLiYhw+fDjoyem9995Dc3OznHnquS7coLm5GQ0NDRgwYABatWoVdGyrq6uxd+9e+dgWFxdjy5YtQZniihUrkJaWJt9cI53zdispKcGWLVuwadMm+VVYWIgRI0Zg06ZNLYId4OR1DCDoeMfbdl944YWorq4Oeu/LL79Efn4+AH15bryf5wsXLkR2djaGDRsWdjkvHG+lU089FR07dsQPP/yAt99+G8OHD3ff8TbaKtsOH3/8sXjiiSfExo0bxe7du0VlZaW44IILRPfu3cWxY8eEECdbeufk5Ijrr79ebN26VSxZskSkpqa26JZ+yimniD//+c/iiy++ENOmTdPslt6+fXvxxhtviM2bN4vhw4c72i19z549YuPGjWL69Omibdu2YuPGjWLjxo2ivr5eCHGyi++zzz4rtmzZIr766isxf/58kZqaKqZOnSqvQ+q6OHHiRPHFF1+IefPmaXZL9/v9YtGiRWLbtm3i5ptvFu3btw/qIeAm8ZZeLfX19fLxBCDmzJkjNm7cKPbs2SPq6+vFH//4R1FVVSV27dol3n33XdG/f39x1llnyee8ECe7b5533nli7dq14sMPPxRnnXVWUPdNPdeF3SZPnixWr14tdu3aJTZv3iwmT54skpKSxDvvvCOEONktvXPnzuK9994T69evbzEEhdRdd8iQIWLTpk1i+fLlIisrS7O7brhz3mnKXjo7duwQDz74oFi/fr3YtWuXeOONN0S3bt3EwIED5eXjcbvXrVsnTjnlFPHwww+Lr776Srz44osiNTVVvPDCC/IyevLceDzPhTjZe7Rz585i0qRJQe979XgLIcTy5cvFsmXLxM6dO8U777wj+vbtK4qKiuQe1W463q4MeDZv3iwGDRokOnToIPx+v+jSpYu49dZb5X79ks8++0xcdNFFwu/3i9NPP13MnDmzxbr+8Y9/iLPPPlv4fD7Rq1cv8eabbwZ93tzcLKZMmSJycnKE3+8XJSUlorq62tLtC2fkyJECQIuXNJbDsmXLRL9+/UTbtm3FqaeeKvr27Sueeuop0dTUFLSelStXin79+gmfzye6desmFi5c2OK3nnjiCdG5c2fh8/nE+eefLz7++GMbtjB68ZZetZUrV2oe25EjR4qjR4+KIUOGiKysLNGqVSuRn58vxo4d2yKg++6778R1110n2rZtK9LS0sSNN94oB8MSPdeFnW666SaRn58vfD6fyMrKEiUlJXKwI4QQP/30k7j99ttFRkaGSE1NFb/+9a/FwYMHg9axe/ducfnll4s2bdqIzMxMMWHCBHH8+PGgZfSc805SBjx79+4VAwcOlPO4M888U0ycODFoXBYh4nO7/+///k+ce+65wu/3ix49eohnnnkm6HM9eW48nudCCPH2228LAC22x8vH+5VXXhHdunUTPp9P5ObmirKyMnH48GH5czcd7yQhFP2ZiYiIiDwoLsbhISIiIooFAx4iIiLyPAY8RERE5HkMeIiIiMjzGPAQERGR5zHgISIiIs9jwENERESex4CHiIiIPI8BDxEREXkeAx4iIiLyPAY8ROSYpUuXon379mhqagJwcgbppKQkTJ48WV5mzJgx+PWvf420tDT885//DPr+66+/jlNPPRX19fW2ppuI4g8DHiJyzMUXX4z6+nps3LgRALB69WpkZmZi1apV8jKrV6/GsGHDcO2112LhwoVB31+4cCF+85vfoF27dnYmm4jiEAMeInJMeno6+vXrJwc4q1atwrhx47Bx40YcOXIE+/fvx44dO3DJJZdgzJgxePvtt3Hw4EEAwKFDh/DWW2/hpptucnALiCheMOAhIkddcsklWLVqFYQQ+OCDD3DVVVfhnHPOwYcffojVq1ejU6dOOOuss3D++eejV69eWLx4MQDghRdeQH5+PgYOHOjwFhBRPGDAQ0SOuvTSS/Hhhx/is88+Q6tWrdCjRw9ceumlWLVqFVavXo1LLrlEXnbMmDFYtGgRgJPVWTfeeCOSkpIcSjkRxRMGPETkKKkdz9y5c+XgRgp4Vq1ahUsvvVRe9g9/+AP27NmDxx9/HNu2bcPIkSMdSjURxRsGPETkqIyMDPTp0wcvvviiHNwMHDgQn376Kb788sugEp6MjAxcddVVmDhxIoYMGYIzzjjDoVQTUbxhwENEjrvkkkvQ1NQkBzwdOnRAz549kZubi4KCgqBlR48ejcbGRjZWJiJDkoQQwulEEBHp9fe//x3jxo3DgQMH4PP5nE4OEcWJU5xOABGRHkePHsXBgwcxc+ZM3HLLLQx2iMgQVmkRUVyYNWsWevTogdzcXJSXlzudHCKKM6zSIiIiIs9jCQ8RERF5HgMeIiIi8jwGPEREROR5DHiIiIjI8xjwEBERkecx4CEiIiLPY8BDREREnseAh4iIiDyPAQ8RERF53v8HGIjgnm2Yg7QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 打开下载的.nc文件\n",
    "nc_file = nc.Dataset(r'C:\\Users\\calibre66\\Desktop\\WaterYield1981-2022\\wy_0p5deg_yearly_1981_2022.nc', 'r')  # 'r'表示只读模式\n",
    "\n",
    "# print(nc_file.variables.keys()) #dict_keys(['wy', 'lon', 'lat', 'time'])\n",
    "latitude = nc_file.variables['lat'][:] # latitude与longtitude都是一维数组\n",
    "longitude = nc_file.variables['lon'][:]\n",
    "X, Y = np.meshgrid(longitude, latitude) # 合并成二维矩阵\n",
    "time = nc_file.variables['time'][:]\n",
    "wy = nc_file.variables['wy'] # E是三维数组\n",
    "\n",
    "print(longitude.shape,latitude.shape)\n",
    "print(X.shape, Y.shape, wy.shape) #查看数组格式\n",
    "\n",
    "plt.contourf(X, Y,  wy[41, :, :].T) # 1981-2022, 42年，# 该文件 经纬度是反的\n",
    "plt.colorbar(label=\"wy\", orientation=\"horizontal\")  # horizontal, vertical\n",
    "plt.show()\n",
    "nc_file.close() # 关闭nc文件\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ca982ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# 模块导入    \n",
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "from osgeo import gdal,osr,ogr\n",
    "import os\n",
    "import glob\n",
    "# 单个nc数据ndvi数据读取为多个tif文件，并将ndvi值化为-1-1之间\n",
    "def NC_to_tiffs(data,Output_folder,key):\n",
    "    nc_data_obj = nc.Dataset(data)\n",
    "    Lon = nc_data_obj.variables['lon'][:]\n",
    "    Lat = nc_data_obj.variables['lat'][:]\n",
    "    arr = np.asarray(nc_data_obj.variables[key])  #将ndvi数据读取为数组\n",
    "    # arr_float = arr.astype(float)/10000 #将int类型改为float类型,并化为-1 - 1之间\n",
    "    #影像的左上角和右下角坐标\n",
    "    LonMin,LatMax,LonMax,LatMin = [Lon.min(),Lat.max(),Lon.max(),Lat.min()] \n",
    "    #分辨率计算\n",
    "    N_Lat = len(Lat) \n",
    "    N_Lon = len(Lon)\n",
    "    Lon_Res = (LonMax - LonMin) /(float(N_Lon)-1)\n",
    "    Lat_Res = (LatMax - LatMin) / (float(N_Lat)-1)\n",
    "    for i in range(len(arr[:])):  # 0维大小\n",
    "        #创建.tif文件\n",
    "        driver = gdal.GetDriverByName('GTiff')\n",
    "        out_tif_name = Output_folder + '\\\\'+ data.split('\\\\')[-1].split('.')[0] + '_' + str(i+1) + '.tif'\n",
    "        out_tif = driver.Create(out_tif_name,N_Lon,N_Lat,1,gdal.GDT_Float32) \n",
    "        # 设置影像的显示范围\n",
    "        #-Lat_Res一定要是-的\n",
    "        geotransform = (LonMin,Lon_Res, 0, LatMax, 0, -Lat_Res)\n",
    "        out_tif.SetGeoTransform(geotransform)\n",
    "        #获取地理坐标系统信息，用于选取需要的地理坐标系统\n",
    "        srs = osr.SpatialReference()\n",
    "        srs.ImportFromEPSG(4326) # 定义输出的坐标系为\"WGS 84\"，AUTHORITY[\"EPSG\",\"4326\"]\n",
    "        out_tif.SetProjection(srs.ExportToWkt()) # 给新建图层赋予投影信息\n",
    "        #数据写出\n",
    "        out_tif.GetRasterBand(1).WriteArray(ndvi_arr_float[i][::-1]) # 将数据写入内存，此时没有写入硬盘 此处[::-1]用于图像的垂直镜像对称，避免图像颠倒\n",
    "        out_tif.FlushCache() # 将数据写入硬盘\n",
    "        out_tif = None # 注意必须关闭tif文件\n",
    "        \n",
    "if __name__ == '__main__':\n",
    "    Input_folder = 'D:\\\\data'\n",
    "    Output_folder = 'D:\\\\NC2TIFF'\n",
    "    start_year=1981\n",
    "    end_year=2022\n",
    "    # dict_keys=['wy', 'lon', 'lat', 'time']\n",
    "    key= 'wy'\n",
    "    # 读取所有nc数据\n",
    "    data_list = glob.glob(Input_folder + '\\\\*.nc')\n",
    "    for i in range(len(data_list)):\n",
    "        data = data_list[i]\n",
    "        NC_to_tiffs(data,Output_folder)\n",
    "        print (data + '-----转tif成功')\n",
    "    print ('----转换结束----')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
